45 citations · 56 across the 11 of their papers we have counts for
12 papers
Deep Reinforcement Learning for Misbehavior Detection Under Partially Observable V2X Data
Roshan Sedar, Charalampos Kalalas
Misbehavior detection in vehicle-to-everything (V2X) systems is essential for ensuring the semantic correctness of exchanged messages and preventing the dissemination of falsified…
Contrastive Self-Supervised Learning at the Edge: An Energy Perspective
Fernanda Famá, Roberto Pereira, Charalampos Kalalas +4
While contrastive learning (CL) shows considerable promise in self-supervised representation learning, its deployment on resource-constrained devices remains largely underexplored.…
Self-Supervised Learning at the Edge: The Cost of Labeling
Roberto Pereira, Fernanda Famá, Asal Rangrazi +3
Contrastive learning (CL) has recently emerged as an alternative to traditional supervised machine learning solutions by enabling rich representations from unstructured and unlabel…
AI-Driven Vehicle Condition Monitoring with Cell-Aware Edge Service Migration
Charalampos Kalalas, Pavol Mulinka, Guillermo Candela Belmonte +10
Artificial intelligence (AI) has been increasingly applied to the condition monitoring of vehicular equipment, aiming to enhance maintenance strategies, reduce costs, and improve s…
Energy-Efficient Federated Learning for AIoT using Clustering Methods
Roberto Pereira, Fernanda Famá, Charalampos Kalalas +1
While substantial research has been devoted to optimizing model performance, convergence rates, and communication efficiency, the energy implications of federated learning (FL) wit…
Knowledge Transfer for Collaborative Misbehavior Detection in Untrusted Vehicular Environments
Roshan Sedar, Charalampos Kalalas, Paolo Dini +3
Vehicular mobility underscores the need for collaborative misbehavior detection at the vehicular edge. However, locally trained misbehavior detection models are susceptible to adve…
Measuring Data Similarity for Efficient Federated Learning: A Feasibility Study
Fernanda Famá, Charalampos Kalalas, Sandra Lagen +1
In multiple federated learning schemes, a random subset of clients sends in each round their model updates to the server for aggregation. Although this client selection strategy ai…
Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters -- A Bayesian Approach
Pavol Mulinka, Subham Sahoo, Charalampos Kalalas +1
In this paper, a hyperparameter tuning based Bayesian optimization of digital twins is carried out to diagnose various faults in grid connected inverters. As fault detection and di…
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics
Alexander Beattie, Pavol Mulinka, Subham Sahoo +4
Timely and accurate detection of anomalies in power electronics is becoming increasingly critical for maintaining complex production systems. Robust and explainable strategies help…
Boosting 5G on Smart Grid Communication: A Smart RAN Slicing Approach
Dick Carrillo, Charalampos Kalalas, Petra Raussi +7
Fifth-generation (5G) and beyond systems are expected to accelerate the ongoing transformation of power systems towards the smart grid. However, the inherent heterogeneity in smart…
Reconfigurable Intelligent Surface-Aided Grant-Free Access for Uplink URLLC
Dick Carrillo Melgarejo, Charalampos Kalalas, Arthur Sousa de Sena +1
Reconfigurable intelligent surfaces (RISs) have been recently considered as one of the emerging technologies for future communication systems by leveraging the tuning capabilities…
Three-layer Approach to Detect Anomalies in Industrial Environments based on Machine Learning
Daniel Gutierrez-Rojas, Mehar Ullah, Ioannis T. Christou +8
This paper introduces a general approach to design a tailored solution to detect rare events in different industrial applications based on Internet of Things (IoT) networks and mac…